from bob.bio.face.embeddings.pytorch import iresnet50 from bob.bio.face.utils import lookup_config_from_database ( annotation_type, fixed_positions, memory_demanding, ) = lookup_config_from_database(locals().get("database")) def load(annotation_type, fixed_positions=None, memory_demanding=False): return iresnet50(annotation_type, fixed_positions, memory_demanding) pipeline = load(annotation_type, fixed_positions, memory_demanding) transformer = pipeline.transformer transformer = pipeline.transformer
from sklearn.pipeline import make_pipeline from sklearn.preprocessing import FunctionTransformer from bob.bio.base.algorithm import Distance from bob.bio.base.pipelines import PipelineSimple from bob.bio.face.utils import lookup_config_from_database from bob.pipelines import wrap ( annotation_type, fixed_positions, memory_demanding, ) = lookup_config_from_database() from sklearn.base import BaseEstimator, TransformerMixin from bob.bio.face.color import rgb_to_gray class ToGray(TransformerMixin, BaseEstimator): def transform(self, X, annotations=None): return [rgb_to_gray(data)[0:10, 0:10] for data in X] def _more_tags(self): return {"requires_fit": False} def fit(self, X, y=None): return self def load(annotation_type, fixed_positions=None):